{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization & Creation of features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DATA Saved\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import radialProfile\n",
    "import glob\n",
    "from matplotlib import pyplot as plt\n",
    "import pickle\n",
    "from scipy.interpolate import griddata\n",
    "\n",
    "data= {}\n",
    "epsilon = 1e-8\n",
    "#interpolation number\n",
    "N = 300\n",
    "y = []\n",
    "error = []\n",
    "#number of samples from each type (real and deepfake)\n",
    "number_iter = 900\n",
    "\n",
    "\n",
    "#deepfake data\n",
    "psd1D_total = np.zeros([number_iter, N])\n",
    "label_total = np.zeros([number_iter])\n",
    "psd1D_org_mean = np.zeros(N)\n",
    "psd1D_org_std = np.zeros(N)\n",
    "rootdir = 'deepfake/'\n",
    "cont = 0\n",
    "\n",
    "for filename in glob.glob(rootdir+\"*.jpg\"):\n",
    "    \n",
    "    img = cv2.imread(filename,0)\n",
    "    # Crop image to get inner face\n",
    "    h = int(img.shape[0]/3)\n",
    "    w = int(img.shape[1]/3)\n",
    "    img = img[h:-h,w:-w]\n",
    "    # Calculate FFT\n",
    "    f = np.fft.fft2(img)\n",
    "    fshift = np.fft.fftshift(f)\n",
    "    magnitude_spectrum = 20*np.log(np.abs(fshift))\n",
    "    # Calculate the azimuthally averaged 1D power spectrum\n",
    "    psd1D = radialProfile.azimuthalAverage(magnitude_spectrum)\n",
    "\n",
    "    # Interpolation\n",
    "    points = np.linspace(0,N,num=psd1D.size) \n",
    "    xi = np.linspace(0,N,num=N) \n",
    "    interpolated = griddata(points,psd1D,xi,method='cubic')\n",
    "    \n",
    "    # Normalization\n",
    "    interpolated /= interpolated[0]\n",
    "\n",
    "    psd1D_total[cont,:] = interpolated             \n",
    "    label_total[cont] = 0\n",
    "    cont+=1\n",
    "\n",
    "    if cont == number_iter:\n",
    "        break\n",
    "    \n",
    "for x in range(N):\n",
    "    psd1D_org_mean[x] = np.mean(psd1D_total[:,x])\n",
    "    psd1D_org_std[x]= np.std(psd1D_total[:,x])\n",
    "\n",
    "\n",
    "## real data\n",
    "psd1D_total2 = np.zeros([number_iter, N])\n",
    "label_total2 = np.zeros([number_iter])\n",
    "psd1D_org_mean2 = np.zeros(N)\n",
    "psd1D_org_std2 = np.zeros(N)\n",
    "rootdir2 = 'real/'\n",
    "cont = 0\n",
    "\n",
    "for filename in glob.glob(rootdir2+\"*.jpg\"):   \n",
    "    \n",
    "    img = cv2.imread(filename,0)\n",
    "    # Crop image to get inner face\n",
    "    h = int(img.shape[0]/3)\n",
    "    w = int(img.shape[1]/3)\n",
    "    img = img[h:-h,w:-w]\n",
    "    # Calculate FFT\n",
    "    f = np.fft.fft2(img)\n",
    "    fshift = np.fft.fftshift(f)\n",
    "    fshift += epsilon\n",
    "    magnitude_spectrum = 20*np.log(np.abs(fshift))\n",
    "    # Calculate the azimuthally averaged 1D power spectrum\n",
    "    psd1D = radialProfile.azimuthalAverage(magnitude_spectrum)\n",
    "\n",
    "    # Interpolation\n",
    "    points = np.linspace(0,N,num=psd1D.size) # coordinates of a\n",
    "    xi = np.linspace(0,N,num=N) # coordinates for interpolation\n",
    "    interpolated = griddata(points,psd1D,xi,method='cubic')\n",
    "\n",
    "    # Normalization\n",
    "    interpolated /= interpolated[0]\n",
    "\n",
    "    psd1D_total2[cont,:] = interpolated             \n",
    "    label_total2[cont] = 1\n",
    "    cont+=1\n",
    "    \n",
    "    if cont == number_iter:\n",
    "        break\n",
    "\n",
    "for x in range(N):\n",
    "    psd1D_org_mean2[x] = np.mean(psd1D_total2[:,x])\n",
    "    psd1D_org_std2[x]= np.std(psd1D_total2[:,x])\n",
    "    \n",
    "    \n",
    "y.append(psd1D_org_mean)\n",
    "y.append(psd1D_org_mean2)\n",
    "\n",
    "error.append(psd1D_org_std)\n",
    "error.append(psd1D_org_std2)\n",
    "\n",
    "psd1D_total_final = np.concatenate((psd1D_total,psd1D_total2), axis=0)\n",
    "label_total_final = np.concatenate((label_total,label_total2), axis=0)\n",
    "\n",
    "data[\"data\"] = psd1D_total_final\n",
    "data[\"label\"] = label_total_final\n",
    "\n",
    "output = open('croppedDeepFake_1000.pkl', 'wb')\n",
    "pickle.dump(data, output)\n",
    "output.close()\n",
    "\n",
    "print(\"DATA Saved\") \n",
    "\n",
    "\n",
    "x = np.arange(0, N, 1)\n",
    "fig, ax = plt.subplots(figsize=(15, 9))\n",
    "ax.plot(x, y[0], alpha=0.5, color='red', label='fake', linewidth =2.0)\n",
    "ax.fill_between(x, y[0] - error[0], y[0] + error[0], color='red', alpha=0.2)\n",
    "\n",
    "ax.plot(x, y[1], alpha=0.5, color='blue', label='real', linewidth = 2.0)\n",
    "ax.fill_between(x, y[1] - error[1], y[1] + error[1], color='blue', alpha=0.2)\n",
    "\n",
    "\n",
    "ax.set_title('Satistics 1D Power Spectrum',size=20)\n",
    "plt.xlabel('Spatial Frequency', fontsize=20)\n",
    "plt.ylabel('Power Spectrum', fontsize=20)\n",
    "plt.tick_params(axis='x', labelsize=20)\n",
    "plt.tick_params(axis='y', labelsize=20)\n",
    "ax.legend(loc='best', prop={'size': 20})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average SVM: 0.8744444444444444\n",
      "Average LR: 0.7955555555555556\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "\n",
    "#number of runs\n",
    "num = 5\n",
    "SVM = 0\n",
    "LR = 0\n",
    "\n",
    "for z in range(num):\n",
    "    # read python dict back from the file\n",
    "    pkl_file = open('croppedDeepFake_1000.pkl', 'rb')\n",
    "    \n",
    "    data = pickle.load(pkl_file)\n",
    "    pkl_file.close()\n",
    "    X = data[\"data\"]\n",
    "    y = data[\"label\"]\n",
    "    X.shape\n",
    "    try:\n",
    "\n",
    "        from sklearn.model_selection import train_test_split\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)\n",
    "\n",
    "        from sklearn.svm import SVC\n",
    "        svclassifier = SVC(C=2.76, kernel='rbf', gamma=0.80)\n",
    "        svclassifier.fit(X_train, y_train)\n",
    "        #print('Accuracy on test set: {:.3f}'.format(svclassifier.score(X_test, y_test)))\n",
    "               \n",
    "        \n",
    "        from sklearn.linear_model import LogisticRegression\n",
    "        logreg = LogisticRegression(solver='liblinear', max_iter=1000)\n",
    "        logreg.fit(X_train, y_train)\n",
    "        #print('Accuracy on test set: {:.3f}'.format(logreg.score(X_test, y_test)))\n",
    "        \n",
    "        SVM+=svclassifier.score(X_train, y_train)\n",
    "        LR+=logreg.score(X_test, y_test)\n",
    "        \n",
    "    except:\n",
    "        num-=1\n",
    "        print(num)\n",
    "    \n",
    "\n",
    "print(\"Average SVM: \"+str(SVM/num))\n",
    "print(\"Average LR: \"+str(LR/num))"
   ]
  }
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